Sleep is now known to play a critical role in facilitating the long-term storage of memories. Some of the most intriguing findings in the literature suggest that sleep does not simply support the storage of individual memories, but can help to uncover relationships between experiences that were not apparent while awake - it can provide new insight into these experiences. This phenomenon has far-reaching implications for how we make sense of our environment, but it is a mystery how sleep accomplishes this. The proposed research will help us understand how sleep is able to promote insight into the experiences we have while awake. Simulations from neural network computational models suggest that the key to generalization is to experience related pieces of information many times, in many different orders - to interleave the information. Our environment usually does not provide us with this kind of interleaved exposure to new information. We propose that sleep may provide an opportunity to replay memories from the day in an interleaved fashion, allowing for discovery of the relationships between them. Our first aim is to run an experiment that tests this novel hypothesis, in which human participants will learn about the features of three categories of related objects. At the very end of this training, participants will be exposed to two new objects that have features spanning two of the categories, creating a semantic bridge between the categories. To test generalization, we will then show participants novel stimuli, some of which will have new configurations of features spanning the bridged categories, and ask them to rate how realistic they seem. We expect that the interleaving of the bridge stimuli with the other stimuli during sleep will allow participants who sleep in between two test periods to improve in this generalization, such that they become more likely to feel that novel stimuli with features from the bridged categories are realistic. We will record sleep polysomnographically with high-density electroencephalography, allowing us to determine which sleep stages and specific features of sleep are associated with the improved generalization. Our second aim is to extend our existing computational model of learning during sleep to incorporate sleep physiology, and demonstrate how specific features of sleep might contribute to the pattern of improved generalization performance. This research will help us understand the mechanisms of learning and memory consolidation in the brain, which may in turn lead to insights into how to treat neurological disorders with learning or memory disturbances.